Firewall Management ServiceMCP Configuration & Schema Registry
The Firewall Management Service Model Context Protocol (MCP) configuration provides a validated, machine-readable JSON schema and executable bridge that connects state-of-the-art AI coding assistants — including Claude Desktop, Cursor IDE, Windsurf, Cline, and VS Code Copilot — directly to the Firewall Management Service REST API. By leveraging the standardized open Model Context Protocol, AI agents can dynamically discover capabilities, validate input parameters against strict JSON Schemas, and execute live API operations without context switching or manual copy-pasting.
Quick Specs & Integration Summary
Technical Architecture & Protocol Semantics
Under the Model Context Protocol specification, the Firewall Management Service configuration functions as an isolated protocol adapter. When an AI agent initializes a session, the client establishes a bidirectional JSON-RPC 2.0 communication channel over standard input/output (stdio) or Server-Sent Events (SSE). During the initial handshake, the server publishes its tool manifest extracted from the Firewall Management Service OpenAPI specification (version 2018-01-01).
The Firewall Management Service API is a powerful programmatic interface provided by Amazon Web Services (AWS) designed to centrally manage firewall configurations, policies, and compliance across an entire organization's infrastructure. It serves as the backend engine for the AWS Firewall Manager console, enabling administrators to define, deploy, and enforce consistent security policies for services like AWS WAF, AWS Shield Advanced, AWS Network Firewall, and Amazon VPC Security Groups. This API is critical for enterprise and large-scale consumer use cases where security governance, uniform policy application, and real-time compliance monitoring across multiple AWS accounts and regions are paramount. It allows security teams to automate the creation of firewall rules, manage third-party firewall integrations, and orchestrate resource associations, thereby reducing manual overhead and ensuring a robust security posture across complex, multi-account environments. Exposing this API as a set of tools through a Model Context Protocol (MCP) server unlocks significant value for AI coding assistants such as Claude Desktop, Cursor, or Cline. It transforms the assistant from a passive code generator into an active, security-aware operations partner. With direct access to Firewall Manager's capabilities, the AI can query current policy states, validate compliance configurations, and programmatically manage firewall resources. This integration bridges the gap between high-level security intent and low-level API implementation, allowing developers to interact with their security infrastructure using natural language commands. The AI can leverage the API's comprehensive toolset to audit configurations, draft policy changes based on natural language requirements, and even execute approved deployments, significantly accelerating development workflows while embedding security practices directly into the creation process. Practical workflow examples for an AI agent using this MCP server are numerous and impactful. A developer could instruct the agent with commands like, "Query all currently active Firewall Manager policies and list the accounts they protect," enabling rapid visibility into the organization's security landscape. More dynamically, a user might say, "Create a new WAF policy for SQL injection protection and associate it with all accounts in the 'Production' organizational unit," guiding the AI through the multi-step process of policy creation and resource association. The agent could also perform complex audits by stating, "Check for any unassociated third-party firewalls and generate a remediation report," or automate cleanup tasks with, "Delete the outdated 'LegacyProtocols' policy and disassociate it from all resources." These workflows demonstrate how the MCP integration allows the AI to act as an executor for complex, multi-API calls, handling the intricate details of pagination, error handling, and state management on behalf of the developer. When setting up a server for this API, developers must treat authentication and security as non-negotiable priorities. The API itself employs standard AWS API signature verification, meaning any integration must handle secure credential management. The MCP server implementation must securely store and use AWS credentials with the principle of least privilege, strictly limiting permissions to only the specific Firewall Manager API actions and resources required. For example, an AI assistant for policy auditing would need read-only permissions (`fms:ListPolicies`, `fms:GetPolicy`), while one for deployment would need write permissions (`fms:CreatePolicy`, `fms:AssociateAdminAccount`) scoped to specific policies or organizational units. Developers should never embed long-term credentials directly; instead, they should use IAM roles with temporary credentials, environment variables, or secure secret managers. Configuration should also include clear user consent and approval mechanisms for any state-changing operations, ensuring that while the AI agent is powerful, all actions remain under strict human oversight and alignment with organizational security governance. This architecture guarantees strict process boundary isolation: all sensitive authorization headers and secret tokens remain sandboxed inside the client runtime, never leaking into language model context windows or external logging endpoints.
Hosted Remote Configuration URL
MCP Configuration FileProvide this hosted URL in any client that supports remote MCP schema auto-loading.
https://mcpbridge.org/config/amazonaws-com-fms.json2. AI Assistant Use Cases & Practical Workflows
Tailored for Cloud InfrastructureReal-world execution scenarios demonstrating how LLM agents (Claude 3.7, GPT-4o, Cursor Agent) invoke Firewall Management Service tools to automate developer workflows.
1. CI/CD Build Failure & Telemetry Diagnostics
CI/CD RemediationInstantly diagnose failing CI/CD builds or deployment pipelines by streaming build logs, isolating failure root causes, and drafting targeted code fixes.
"Fetch recent pipeline run logs from Firewall Management Service. Isolate the failed step, summarize the exact compiler or test failure error, and propose a pull request fix in Cursor."
2. Cloud Resource Auditing & Cost Optimization
Cloud FinOpsScan active compute clusters, storage buckets, and networking configurations to identify unattached volumes or idle oversized instances.
"Query active cloud infrastructure resources in Firewall Management Service. Identify unattached storage volumes, idle compute instances, and summarize estimated monthly cost savings."
3. Zero-Downtime Rollout & Canary Health Verification
Deployment OpsOrchestrate progressive deployments, monitor error rate thresholds on newly deployed pods, and execute automated rollbacks if error budgets breach.
"Check the active deployment rollout status in Firewall Management Service. Monitor canary error rate percentages for 5 minutes and report whether the deployment is safe to promote to 100% traffic."
4. Infrastructure as Code (IaC) Drift Detection
IaC GovernanceCompare live deployed resource state against Terraform or CloudFormation definitions to spot unauthorized manual changes.
"Scan live configurations via Firewall Management Service and compare against our repository IaC definitions. Highlight any configuration drift in security groups or network routes."
End-to-End Multi-Step Agent Execution Lifecycle
When an engineer submits a task to Claude Desktop or Cursor, the LLM executes an autonomous 4-phase Model Context Protocol loop:
Schema Introspection
Handshake lists all 10 tools and builds argument validators.
Argument Synthesis
Model extracts parameters from prompt and validates types against OpenAPI rules.
Stdio Execution
Bridge invokes live API with injected local credentials and captures raw HTTP response.
Output Remediation
LLM parses JSON results, handles status codes, and presents synthesized answers.
3. Multi-Client Installation Matrix & Setup Guides
Select your AI assistant below to view exact configuration file paths, JSON installation snippets, and launch commands.
Claude Desktop
claude_desktop_config.json~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json~/.config/Claude/claude_desktop_config.json{
"mcpServers": {
"amazonaws-com-fms": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-openapi",
"https://api.apis.guru/v2/specs/amazonaws.com/fms/2018-01-01/openapi.json"
],
"env": {
"FIREWALL_MANAGEMENT_SERVICE_API_KEY": "your_firewall_management_service_api_key"
}
}
}
}Cursor IDE
.cursor/mcp.jsonOpen Cursor Settings → Features → MCP Servers, or create .cursor/mcp.json in your project root.
{
"mcpServers": {
"amazonaws-com-fms": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-openapi",
"https://api.apis.guru/v2/specs/amazonaws.com/fms/2018-01-01/openapi.json"
],
"env": {
"FIREWALL_MANAGEMENT_SERVICE_API_KEY": "your_firewall_management_service_api_key"
}
}
}
}Saves as .cursor/mcp.json in the download. Move it to your project root.
VS Code / Cline Extension
cline_mcp_settings.jsonPaste into your Cline extension MCP configuration or Roo Code host settings.
{
"mcpServers": {
"amazonaws-com-fms": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-openapi",
"https://api.apis.guru/v2/specs/amazonaws.com/fms/2018-01-01/openapi.json"
],
"env": {
"FIREWALL_MANAGEMENT_SERVICE_API_KEY": "your_firewall_management_service_api_key"
}
}
}
}Zed Editor & Docker CLI
Zed / DockerDocker container execution command:
docker run -i --rm -e FIREWALL_MANAGEMENT_SERVICE_API_KEY="YOUR_SECRET_VALUE" node:20-alpine npx -y @modelcontextprotocol/server-openapi https://api.apis.guru/v2/specs/amazonaws.com/fms/2018-01-01/openapi.json
Zed settings context servers JSON:
{
"context_servers": {
"amazonaws-com-fms": {
"command": {
"path": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-openapi",
"https://api.apis.guru/v2/specs/amazonaws.com/fms/2018-01-01/openapi.json"
],
"env": {
"FIREWALL_MANAGEMENT_SERVICE_API_KEY": "your_firewall_management_service_api_key"
}
}
}
}
}Programmatic SDK Integration (TypeScript / Python)
Initialize the Firewall Management Service MCP client directly in your backend codebase.
import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StdioClientTransport } from "@modelcontextprotocol/sdk/client/stdio.js";
// Initialize Firewall Management Service MCP client transport over stdio
const transport = new StdioClientTransport({
command: "npx",
args: ["-y","@modelcontextprotocol/server-openapi","https://api.apis.guru/v2/specs/amazonaws.com/fms/2018-01-01/openapi.json"],
env: { FIREWALL_MANAGEMENT_SERVICE_API_KEY: process.env.FIREWALL_MANAGEMENT_SERVICE_API_KEY || "YOUR_SECRET_KEY" }
});
const client = new Client(
{ name: "amazonaws-com-fms-client", version: "1.0.0" },
{ capabilities: { tools: {}, resources: {}, prompts: {} } }
);
async function connectAndRun() {
await client.connect(transport);
const tools = await client.listTools();
console.log("Connected to Firewall Management Service MCP Server.");
console.log("Discovered 10 mapped tools:", tools);
}
connectAndRun().catch(console.error);Raw Stdio Schema Definition
schema.jsonFor standalone CLI wrappers, background daemon daemons, or custom script integrations:
{
"mcpServers": {
"amazonaws-com-fms": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-openapi",
"https://api.apis.guru/v2/specs/amazonaws.com/fms/2018-01-01/openapi.json"
],
"env": {
"FIREWALL_MANAGEMENT_SERVICE_API_KEY": "your_firewall_management_service_api_key"
}
}
}
}4. Security, Authentication & Credential Management
Safely configure authentication tokens, isolate execution environments, and implement enterprise security best practices.
Required Environment Keys Reference
| Variable Name | Required | Type | Default | Purpose & Guidance |
|---|---|---|---|---|
| FIREWALL_MANAGEMENT_SERVICE_API_KEY | REQUIRED | Secret Key / Token | None (Set in env) | your_firewall_management_service_api_key |
Zero-Downtime Token Rotation Protocol
- Generate Secondary Key: Create a new secret API token with identical scopes in your Firewall Management Service developer portal.
- Update Client Configuration: Insert the new token inside the
envblock of your MCP client JSON config. - Validate Connection: Issue a test query in Claude or Cursor to ensure handshake and tool calls succeed.
- Revoke Stale Token: Decommission the legacy key on the vendor portal to prevent unauthorized access.
Least-Privilege & Sandboxing Rules
- Read-Only Token Scoping: Whenever your workflow only requires querying data, provision read-only credentials to prevent accidental mutations.
- Local Process Isolation: Stdio transports run in isolated local subprocesses; secret credentials are never sent across the internet to MCP Bridge servers.
- Prompt Injection Defense: AI model responses are sandboxed; verify generated destructive arguments before confirming execution in agent mode.
Enterprise Security Checklist (Mandatory Practices)
- Never commit
claude_desktop_config.jsonor.cursor/mcp.jsoncontaining raw secrets into public GitHub repositories. - Add
.cursor/mcp.jsonand.env.localto your project's.gitignorefile. - Always enforce TLS/HTTPS encryption on outbound network requests initiated by the server process.
5. Tool Parameter Schemas & Natural Language Execution
Mapped OpenAPI operations converted into discrete Model Context Protocol tools with strict JSON-RPC payload validators.
/#X-Amz-Target=AWSFMS_20180101.AssociateAdminAccountAssociateAdminAccount
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "amazonaws-com-fms_post_X_Amz_Target_AWSFMS_20180101_AssociateAdminAccount",
"arguments": {}
}
}"Use Firewall Management Service to execute AssociateAdminAccount and output the formatted result."
/#X-Amz-Target=AWSFMS_20180101.AssociateThirdPartyFirewallAssociateThirdPartyFirewall
{
"jsonrpc": "2.0",
"id": 2,
"method": "tools/call",
"params": {
"name": "amazonaws-com-fms_post_X_Amz_Target_AWSFMS_20180101_AssociateThirdPartyFirewall",
"arguments": {}
}
}"Use Firewall Management Service to execute AssociateThirdPartyFirewall and output the formatted result."
/#X-Amz-Target=AWSFMS_20180101.BatchAssociateResourceBatchAssociateResource
{
"jsonrpc": "2.0",
"id": 3,
"method": "tools/call",
"params": {
"name": "amazonaws-com-fms_post_X_Amz_Target_AWSFMS_20180101_BatchAssociateResource",
"arguments": {}
}
}"Use Firewall Management Service to execute BatchAssociateResource and output the formatted result."
/#X-Amz-Target=AWSFMS_20180101.BatchDisassociateResourceBatchDisassociateResource
{
"jsonrpc": "2.0",
"id": 4,
"method": "tools/call",
"params": {
"name": "amazonaws-com-fms_post_X_Amz_Target_AWSFMS_20180101_BatchDisassociateResource",
"arguments": {}
}
}"Use Firewall Management Service to execute BatchDisassociateResource and output the formatted result."
/#X-Amz-Target=AWSFMS_20180101.DeleteAppsListDeleteAppsList
{
"jsonrpc": "2.0",
"id": 5,
"method": "tools/call",
"params": {
"name": "amazonaws-com-fms_post_X_Amz_Target_AWSFMS_20180101_DeleteAppsList",
"arguments": {}
}
}"Use Firewall Management Service to execute DeleteAppsList and output the formatted result."
/#X-Amz-Target=AWSFMS_20180101.DeleteNotificationChannelDeleteNotificationChannel
{
"jsonrpc": "2.0",
"id": 6,
"method": "tools/call",
"params": {
"name": "amazonaws-com-fms_post_X_Amz_Target_AWSFMS_20180101_DeleteNotificationChannel",
"arguments": {}
}
}"Use Firewall Management Service to execute DeleteNotificationChannel and output the formatted result."
/#X-Amz-Target=AWSFMS_20180101.DeletePolicyDeletePolicy
{
"jsonrpc": "2.0",
"id": 7,
"method": "tools/call",
"params": {
"name": "amazonaws-com-fms_post_X_Amz_Target_AWSFMS_20180101_DeletePolicy",
"arguments": {}
}
}"Use Firewall Management Service to execute DeletePolicy and output the formatted result."
/#X-Amz-Target=AWSFMS_20180101.DeleteProtocolsListDeleteProtocolsList
{
"jsonrpc": "2.0",
"id": 8,
"method": "tools/call",
"params": {
"name": "amazonaws-com-fms_post_X_Amz_Target_AWSFMS_20180101_DeleteProtocolsList",
"arguments": {}
}
}"Use Firewall Management Service to execute DeleteProtocolsList and output the formatted result."
6. Interactive Troubleshooting & FAQ Accordion
Diagnose and resolve common JSON-RPC protocol error codes, connection disconnects, and schema refresh issues.
A 401 Unauthorized response indicates that the upstream Firewall Management Service API rejected the authentication credential supplied in your MCP client's environment configuration. To resolve this: (1) Verify that your secret token is defined inside the "env" block of claude_desktop_config.json or .cursor/mcp.json rather than hardcoded in the command string. (2) Check whether Firewall Management Service requires a prefix such as "Bearer <token>" in the authorization header. (3) Confirm that your API key has not expired and has been granted sufficient least-privilege scopes on the Firewall Management Service developer dashboard.
If your MCP client fails to initialize tools for Firewall Management Service: (1) Test the bridge launcher command ("npx -y @modelcontextprotocol/server-openapi https://api.apis.guru/v2/specs/amazonaws.com/fms/2018-01-01/openapi.json") directly inside your terminal or shell to inspect stdout/stderr diagnostic traces. (2) Verify network connectivity to the schema source (https://api.apis.guru/v2/specs/amazonaws.com/fms/2018-01-01/openapi.json). (3) Ensure Node.js (v18+) is installed and accessible in your system PATH. (4) For authenticated APIs, confirm credentials are configured in your client's "env" mapping rather than command arguments.
MCP clients like Claude Desktop and Cursor query the server's tools list ("tools/list") during startup and cache the resulting JSON Schema for the duration of the application session. If new endpoints or parameters are added to Firewall Management Service: (1) Fully quit and restart Claude Desktop (Cmd+Q on macOS or File > Exit on Windows). (2) In Cursor IDE, navigate to Settings > Features > MCP Servers, toggle the Firewall Management Service server off and on, or click the refresh icon to re-execute the initialization handshake.
If the AI model hallucinates parameters or fails to invoke a tool automatically: (1) Add explicit system instructions in your project's .cursorrules or Claude project prompt (e.g., "When querying Cloud Infrastructure, always invoke the amazonaws-com-fms MCP server tools first"). (2) Ensure parameter types match schema specifications (e.g., passing integers as numbers rather than strings). (3) Check that required parameters marked in Section 5 are not omitted from the model's generated payload.
When the Firewall Management Service upstream endpoint returns an HTTP 429 Too Many Requests response, the MCP server bubbles the structured error payload back to the AI client over stdio. Modern LLMs like Claude 3.7 and Cursor Agent recognize rate-limiting status codes, inspect the "Retry-After" header if present, and will automatically introduce backoff delays or ask the user before retrying the operation.
The Hosted Config URL (https://mcpbridge.org/config/amazonaws-com-fms.json) provides a static, remote JSON schema definition that cloud-native MCP clients can fetch over HTTPS for dynamic discovery. In contrast, local stdio configurations execute a local subprocess on your workstation. Local stdio processes offer maximum security because secret API keys remain strictly on your local machine and never transit third-party proxy servers.
Similar Cloud Infrastructure Configurations
Explore related API bridges with ready-to-use Model Context Protocol schemas.
Supabase API
Cloud InfrastructureManage Supabase projects, databases, authentication, and storage through your AI agent.
https://mcpbridge.org/config/supabase.jsonCloudflare API
Cloud InfrastructureManage Cloudflare DNS, CDN, Workers, and security settings through your AI agent.
https://mcpbridge.org/config/cloudflare.jsonVercel API
Cloud InfrastructureDeploy projects, manage domains, and monitor deployments through your AI agent.
https://mcpbridge.org/config/vercel.jsonDigitalOcean API
Cloud InfrastructureThe DigitalOcean API is a comprehensive, RESTful interface provided by DigitalOcean, a leading cloud infrastructure provider focused on simplifying cloud computing for developers, startups, and enterprises. It serves as the programmatic backbone for managing the entire DigitalOcean ecosystem, enabling users to provision, configure, and control cloud resources such as Droplets (virtual private servers), Kubernetes clusters, managed databases, networks, storage volumes, and application platforms. Core capabilities include full lifecycle management of these resources, from creation and scaling to monitoring and deletion, mirroring the functionality available in the DigitalOcean control panel. Its primary use cases range from automating infrastructure setup for CI/CD pipelines and enabling infrastructure-as-code practices to supporting dynamic application scaling and resource optimization for SaaS products, e-commerce sites, and development environments. The API is designed for both developers seeking to automate their cloud operations and businesses that require programmable, scalable cloud infrastructure without the complexity of larger hyperscale providers. When exposed as tools via the Model Context Protocol (MCP) to an AI coding assistant, the DigitalOcean API transforms from a traditional developer tool into a dynamic, context-aware resource for intelligent infrastructure automation. The MCP server acts as a bridge, allowing the AI model to understand and execute API calls based on natural language instructions and the current project context. This integration provides immense value by enabling the AI to perform real-time cloud management tasks directly within the development workflow. For instance, the AI can instantly query account details to verify resources, list and manage SSH keys for secure access, or retrieve and monitor the status of infrastructure actions. This contextual access means the AI can make informed suggestions or take automated actions—like recommending a cost-optimized Droplet size based on current usage patterns or verifying that a new SSH key has been correctly added before proceeding with a deployment script—thereby reducing context-switching and accelerating development cycles. Practical workflow examples demonstrate the power of this MCP integration. A developer could instruct the AI agent with commands like, "Query our account for all active SSH keys and ensure the one named 'ci-bot' is present; if not, create it using this public key," automating a common security and setup step. Another example involves asking the AI to "Check the status of our last ten infrastructure actions to see if any are stuck in a 'pending' state," which would leverage the actions endpoints to provide an immediate operational health check. More complex automations are possible, such as "Based on the current Droplet inventory from the API, generate a Terraform configuration file that replicates this setup," or "Scan our Kubernetes 1-Click apps and suggest one for deploying a new microservice based on the project requirements." These interactions turn the AI into a proactive DevOps partner capable of auditing, reporting, and modifying cloud infrastructure through simple, conversational directives. Critical to the secure operation of this MCP server is rigorous attention to authentication and access control, despite any initial configuration notes indicating "None" for simplicity. In any real-world deployment, authentication via a DigitalOcean Personal Access Token is non-negotiable. This token should be treated as a high-privilege secret. Developers must adhere to the principle of least privilege by creating tokens with the minimum scopes required for the specific tasks—such as read-only access for monitoring or write access only for specific resource types. Best practices include storing tokens in secure environment variables or a secrets manager, never hardcoding them, and ensuring the MCP server configuration does not expose them in logs or client-side code. Furthermore, regular token rotation and monitoring of API activity through DigitalOcean's audit logs are essential to maintain a secure posture when integrating cloud management capabilities directly into AI-assisted development environments.
https://mcpbridge.org/config/digitalocean-com.json